fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of brglm2 and nswgeo — release velocity, themes, recent moves, and the top alternatives to consider.
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.
NSW boundary data for R, refreshed as the official sources move
nswgeo packages New South Wales geographic boundaries for R — suburbs, postcodes, local government areas, Primary Health Networks and Local Health Districts — as ready-to-plot sf datasets. The 0.6.0 release refreshes nearly all of them against new upstream sources, moving postcodes to 2021 ABS boundaries and taking LHD boundaries from a new official feed. It is maintained by cidm-ph alongside the mapping packages that consume it, including ggmapinset.
brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.
The package's older work assumed the classical regime where observations comfortably outnumber parameters. mdyplFit() and its hd_correction argument target the opposite case, and the follow-up releases are almost entirely about it — Pearson residuals on original responses, aliased parameter handling, the sloe() signal-strength estimator ignoring leverage-one observations. Meanwhile the older surface gets graceful-failure work: brglm_fit() now returns its latest estimates with warnings rather than aborting.
Given that 1.0.1 and 1.1.0 are both dominated by mdyplFit follow-ups while the classical path receives only robustness fixes, further work on high-dimensional corrections is the likeliest direction.
nswgeo packages New South Wales geographic boundaries for R — suburbs, postcodes, local government areas, Primary Health Networks and Local Health Districts — as ready-to-plot sf datasets. The 0.6.0 release refreshes nearly all of them against new upstream sources, moving postcodes to 2021 ABS boundaries and taking LHD boundaries from a new official feed. It is maintained by cidm-ph alongside the mapping packages that consume it, including ggmapinset.
Every release is dictated by an upstream release calendar rather than a roadmap: the 2023 ASGS, then 2024, then the 2021 ABS postcode boundaries and the new LHD source. That makes field-name churn the package's defining hazard — LGA_NAME_2021 to LGA_NAME_2023 to LGA_NAME_2024, and now lhd_name carrying a Local Health District suffix. The maintainer's habit of registering compatibility aliases through cartographer shows an awareness that these renames break downstream code silently.
Expect the next release to track the following ASGS edition with another round of field renames, and any new content to stay in the health-geography area the package's users work in.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either brglm2 or nswgeo.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
See all brglm2 alternatives → · See all nswgeo alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. brglm2 and nswgeo are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. brglm2 and nswgeo are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top brglm2 alternatives in Analytics are ranked by recent ship velocity. Browse the "brglm2 alternatives" section above for the current picks, or visit /alternatives/brglm2 for the full list with editorial commentary on each.
Top nswgeo alternatives in Analytics are ranked by recent ship velocity. Browse the "nswgeo alternatives" section above for the current picks, or visit /alternatives/nswgeo for the full list with editorial commentary on each.